Mining Composite Spatio-Temporal Lifestyle Patterns from Geotagged Social Data

As social networks become increasingly integrated with their users' daily lives, and users are willing to publicly share data about their offline activities on these networks, the resultant data offers a powerful tool to non-intrusively understand city dynamics as it captures human behaviour and interactions. In this paper, we derive lifestyle patterns from the Foursquare social network data, using matrix factorization and tensor decomposition as unsupervised methods to extract latent spatio-temporal behavior patterns. The extracted patterns offer precise definition of activity levels associated with specific lifestyles and showcase that users' behaviors are a combination of several lifestyles, in contrast to traditional circadian topology theory which classifies individuals to a specific temporal pattern. The obtained patterns can provide deeper insights into city dynamics, the people within them and how society functions.